This criterion assumes that the relationship of geometric structures can reduce the domain shift, and thus it bridges the source and target domains according to their geometrical properties. In particular, it mitigates the domain shift by integrating intermediate subspaces (a fixed [1]} or infinite number [2]}) on a geodesic path between the two domains; both source and target data are projected to the obtained intermediate subspaces to align the distribution. For example, Chopra et al. [3]} proposed a model called deep learning
for da by interpolating between domains (DLID). In particular, starting with all the source data samples and gradually replacing them with the target data samples, DLID creates intermediate datasets; then, a deep feature extractor is trained in an unsupervised fashion, exploiting the sparse predictive decomposition.
